从视频中识别汽车语音
Recognizing Car Fluents from Video
摘要：
物理流畅性，一个最初由牛顿[40]使用的术语，指的是动态场景中随时间变化的对象状态。在本文中，我们感兴趣的是从视频中推断车辆的流畅性。例如，门（引擎盖，行李箱）是通过各种动作打开或关闭，灯是闪烁转向。识别这些流畅性具有广泛的应用，但在计算机视觉文献中很少受到关注。汽车流畅识别需要一个统一的框架，汽车检测，汽车零件定位和零件状态识别，这是困难的大结构和外观变化，低分辨率和遮挡。本文学习了一种时空与或层次模型来表示汽车流。该模型的学习是在潜在结构支持向量机框架下进行的。由于没有公开相关的数据集，我们收集并注释了一个汽车流畅数据集，该数据集由具有不同流畅度的汽车视频组成。实验表明，该方法在汽车流畅识别和汽车零部件定位方面优于几种高度相关的基线方法。
英文摘要：
Physical fluents, a term originally used by Newton [40], refers to time-varying object states in dynamic scenes. In this paper, we are interested in inferring the fluents of vehicles from video. For example, a door (hood, trunk) is open or closed through various actions, light is blinking to turn. Recognizing these fluents has broad applications, yet have received scant attention in the computer vision literature. Car fluent recognition entails a unified framework for car detection, car part localization and part status recognition, which is made difficult by large structural and appearance variations, low resolutions and occlusions. This paper learns a spatial-temporal And-Or hierarchical model to represent car fluents. The learning of this model is formulated under the latent structural SVM framework. Since there are no publicly related dataset, we collect and annotate a car fluent dataset consisting of car videos with diverse fluents. In experiments, the proposed method outperforms several highly related baseline methods in terms of car fluent recognition and car part localization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
